International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
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Quantum Computing Meets Deep Learning: Emerging Techniques and Use Cases Vijayalakshmi S1, Spoorthi G R2, Adil Ahmed3, Sumanth P Bellad4 1Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. 2Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. 3Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. 4Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - One of the most effective theories that has shaped the development of science in the 20th century is quantum theory. It has affected many areas of contemporary technology, introduced a fresh school of scientific thought, and foreseen scenarios that were previously unthinkable. The laws of physics in particular and the laws of science in general can be expressed in a variety of ways. Information can also be conveyed in a variety of ways, much like the physical rules of nature. The potential for automatic information manipulation derives from the notion that information can be conveyed in a variety of ways without losing its fundamental characteristics.
data as matrices necessitates the use of linear algebra to perform matrix operations in many machine learning situations. On the other hand, it takes a lot of time and computational power to complete similar tasks on conventional computers. The ambitious new discipline of quantum computing blends physics, mathematics, and computer technology.
Key Words: computation, EPR, quantum mechanics, superposition, unitary transformation, decoherence, Deep learning , Neural networks 1.INTRODUCTION It appears that business is significantly impacted by quantum computers. The 1980s saw the proposal of quantum computing, which led to the development of numerous quantum algorithms (Benioff, 1980; Coles et al., 2018; Feynman, 1982; Montanaro, 2016). The two most well-known quantum algorithms are the integer factoring algorithm of Shor and Glover's database search algorithm. Both quantum algorithms have been shown to perform noticeably better than classical computer algorithms and to be capable of breaking encryption methods (like AES, RSA, and ECC) that are widely used on the Internet (like online shopping sites). Governments have been boosting financing for research and development of quantum computing for both national security and the growth of computer technologies.
This paper's primary contribution is a visual representation of the development of QC and DL algorithms over the past few years. We therefore ran a number of searches in Web of Science and Scopus to best validate the results that were found. Then, using particular queries from the examined applications, we gathered the data in plain text to create the networks, clusters, and relationships of works worldwide.
A subset of artificial intelligence (AI), machine learning seeks to develop models that learn from past experiences without explicit formulation. It has found widespread application in a variety of scientific and technical domains, such as data mining, computer vision, natural language processing, and medical diagnostics. The description of
Quantum computing (QC) and deep learning (DL) have captured the attention of both academic researchers and industry professionals due to their disruptive potential. Classical computers, constrained by the limits of Moore’s Law, struggle with increasingly complex tasks, making QC an appealing alternative. Meanwhile, DL models have
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Fig-1 AI, ML, and Deep Learning: A Visual Breakdown
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